Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "225" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 28 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 28 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460011 | RF_ok | 100.00% | 0.00% | 87.53% | 0.00% | - | - | -0.161415 | 14.975919 | 0.847489 | 7.221505 | -0.884636 | 15.408933 | -1.359581 | 2.297698 | 0.5828 | 0.1445 | 0.4785 | nan | nan |
| 2460010 | RF_ok | 100.00% | 0.00% | 86.87% | 0.00% | - | - | -0.163450 | 16.293191 | 1.091891 | 6.078314 | -0.470613 | 10.056215 | -1.338605 | 2.102635 | 0.5955 | 0.1491 | 0.4861 | nan | nan |
| 2460009 | RF_ok | 100.00% | 0.00% | 86.27% | 0.00% | - | - | -0.171267 | 15.020706 | 0.935818 | 6.858720 | -0.545814 | 8.479043 | -1.767700 | 2.257084 | 0.5983 | 0.1546 | 0.4869 | nan | nan |
| 2460008 | RF_ok | 100.00% | 0.00% | 60.00% | 0.00% | - | - | -0.243510 | 18.489187 | 1.030575 | 7.390569 | -0.477237 | 7.496037 | -0.142141 | 4.898718 | 0.6324 | 0.1991 | 0.4911 | nan | nan |
| 2460007 | RF_ok | 100.00% | 0.00% | 82.13% | 0.00% | - | - | -0.490464 | 13.918360 | 0.556905 | 5.808059 | -0.772222 | 6.955020 | -1.279221 | 2.365040 | 0.6019 | 0.1552 | 0.4842 | nan | nan |
| 2459999 | RF_ok | 0.00% | 0.00% | 99.42% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.6181 | 0.1033 | 0.2795 | nan | nan |
| 2459998 | RF_ok | 100.00% | 0.00% | 90.32% | 0.00% | - | - | -0.435133 | 11.708280 | 0.542114 | 4.774911 | -0.571828 | 9.878399 | -1.323641 | 1.780768 | 0.5997 | 0.1358 | 0.4951 | nan | nan |
| 2459997 | RF_ok | 100.00% | 0.00% | 89.89% | 0.00% | - | - | -0.272139 | 12.763311 | 0.851472 | 5.217960 | -0.641417 | 9.285670 | -1.824207 | 2.754427 | 0.6108 | 0.1435 | 0.4940 | nan | nan |
| 2459996 | RF_ok | 100.00% | 0.00% | 87.41% | 0.00% | - | - | 0.057299 | 13.692040 | 0.866734 | 6.592055 | -0.762275 | 8.900217 | -1.320179 | 1.160762 | 0.6218 | 0.1511 | 0.5069 | nan | nan |
| 2459995 | RF_ok | 100.00% | 0.00% | 83.24% | 0.00% | - | - | -0.109490 | 13.999973 | 0.844381 | 5.886205 | -0.572624 | 9.159324 | -1.395213 | 0.970558 | 0.6159 | 0.1522 | 0.4976 | nan | nan |
| 2459994 | RF_ok | 100.00% | 0.00% | 89.90% | 0.00% | - | - | -0.505806 | 13.667947 | 0.714949 | 5.123507 | -0.852212 | 9.265966 | -1.216207 | 0.762569 | 0.6108 | 0.1404 | 0.4993 | nan | nan |
| 2459993 | RF_ok | 100.00% | 0.00% | 97.39% | 0.00% | - | - | -0.386305 | 12.838396 | 0.943763 | 4.462579 | -0.684553 | 10.660074 | -1.064981 | 2.380908 | 0.5971 | 0.1095 | 0.5016 | nan | nan |
| 2459991 | RF_ok | 100.00% | 0.00% | 94.81% | 0.00% | - | - | -0.532760 | 15.989284 | 0.837437 | 4.863295 | -0.718906 | 10.462438 | -1.054365 | 0.911865 | 0.6028 | 0.1199 | 0.5129 | nan | nan |
| 2459990 | RF_ok | 100.00% | 0.00% | 91.74% | 0.00% | - | - | -0.497594 | 13.181641 | 0.785641 | 4.614999 | -0.680391 | 10.766344 | -1.290638 | 0.614324 | 0.6067 | 0.1268 | 0.4993 | nan | nan |
| 2459989 | RF_ok | 100.00% | 0.00% | 91.95% | 0.00% | - | - | -0.622871 | 13.318005 | 0.901973 | 4.337721 | -0.767606 | 9.017480 | -1.196924 | 0.329169 | 0.6075 | 0.1291 | 0.5021 | nan | nan |
| 2459988 | RF_ok | 100.00% | 0.00% | 91.95% | 0.00% | - | - | -0.672815 | 15.617793 | 0.799478 | 4.681188 | -0.887185 | 12.895525 | -1.172455 | 0.499788 | 0.6088 | 0.1334 | 0.5010 | nan | nan |
| 2459987 | RF_ok | 100.00% | 0.00% | 89.52% | 0.00% | - | - | -0.562505 | 13.053163 | 0.738944 | 4.852280 | -0.790846 | 7.754582 | -1.342791 | 1.820509 | 0.6110 | 0.1417 | 0.4928 | nan | nan |
| 2459986 | RF_ok | 100.00% | 0.00% | 67.87% | 0.00% | - | - | -0.411426 | 15.975549 | 0.824322 | 5.155710 | -0.845666 | 10.956474 | -0.767693 | 9.022449 | 0.6369 | 0.1888 | 0.4954 | nan | nan |
| 2459985 | RF_ok | 100.00% | 0.00% | 89.08% | 0.00% | - | - | -0.461000 | 14.390586 | 0.680884 | 4.875191 | -0.995882 | 8.345335 | -1.637674 | 1.865166 | 0.6171 | 0.1412 | 0.5056 | nan | nan |
| 2459984 | RF_ok | 100.00% | 0.00% | 81.67% | 0.00% | - | - | -0.368139 | 13.784398 | 0.827147 | 5.159750 | -0.198687 | 11.781676 | -0.084885 | 3.775051 | 0.6314 | 0.1713 | 0.4963 | nan | nan |
| 2459983 | RF_ok | 100.00% | 0.00% | 73.04% | 0.00% | - | - | -0.669788 | 13.628506 | 0.728838 | 4.631454 | -1.114246 | 10.863366 | -0.999414 | 5.711964 | 0.6328 | 0.1853 | 0.4943 | nan | nan |
| 2459982 | RF_ok | 100.00% | 0.00% | 57.86% | 0.00% | - | - | -1.179010 | 11.406553 | 0.116680 | 4.106878 | -0.982090 | 5.147744 | -1.052124 | 2.741084 | 0.6965 | 0.2349 | 0.5184 | nan | nan |
| 2459981 | RF_ok | 100.00% | 0.00% | 91.73% | 0.00% | - | - | -0.696491 | 12.610679 | 0.885659 | 4.739767 | -0.864181 | 12.057906 | -1.473871 | 0.831085 | 0.6163 | 0.1359 | 0.5065 | nan | nan |
| 2459980 | RF_ok | 100.00% | 0.00% | 68.40% | 0.00% | - | - | -0.981217 | 12.062659 | 0.506421 | 4.440364 | -1.209325 | 10.547372 | -0.458912 | 4.589632 | 0.6605 | 0.2060 | 0.4978 | nan | nan |
| 2459979 | RF_ok | 100.00% | 0.00% | 93.51% | 0.00% | - | - | -0.871378 | 12.702381 | 0.536967 | 4.026978 | -0.703798 | 9.843934 | -1.288309 | 0.587711 | 0.6085 | 0.1281 | 0.5090 | nan | nan |
| 2459978 | RF_ok | 100.00% | 0.00% | 93.41% | 0.00% | - | - | -0.819833 | 12.887848 | 0.732450 | 4.331719 | -0.655372 | 10.697958 | -1.197168 | 1.168815 | 0.6098 | 0.1272 | 0.5117 | nan | nan |
| 2459977 | RF_ok | 100.00% | 0.00% | 97.32% | 0.00% | - | - | -0.469378 | 13.566399 | 0.561880 | 4.505508 | -0.635719 | 10.956538 | -1.339956 | 1.209230 | 0.5729 | 0.1287 | 0.4638 | nan | nan |
| 2459976 | RF_ok | 100.00% | 0.00% | 91.30% | 0.00% | - | - | -0.650795 | 13.103691 | 0.621683 | 4.511652 | -0.658617 | 10.589717 | -1.013523 | 1.386269 | 0.6189 | 0.1345 | 0.5147 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 225 | N19 | RF_ok | nn Temporal Variability | 15.408933 | -0.161415 | 14.975919 | 0.847489 | 7.221505 | -0.884636 | 15.408933 | -1.359581 | 2.297698 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 225 | N19 | RF_ok | nn Shape | 16.293191 | -0.163450 | 16.293191 | 1.091891 | 6.078314 | -0.470613 | 10.056215 | -1.338605 | 2.102635 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 225 | N19 | RF_ok | nn Shape | 15.020706 | -0.171267 | 15.020706 | 0.935818 | 6.858720 | -0.545814 | 8.479043 | -1.767700 | 2.257084 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 225 | N19 | RF_ok | nn Shape | 18.489187 | 18.489187 | -0.243510 | 7.390569 | 1.030575 | 7.496037 | -0.477237 | 4.898718 | -0.142141 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 225 | N19 | RF_ok | nn Shape | 13.918360 | -0.490464 | 13.918360 | 0.556905 | 5.808059 | -0.772222 | 6.955020 | -1.279221 | 2.365040 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 225 | N19 | RF_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 225 | N19 | RF_ok | nn Shape | 11.708280 | -0.435133 | 11.708280 | 0.542114 | 4.774911 | -0.571828 | 9.878399 | -1.323641 | 1.780768 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 225 | N19 | RF_ok | nn Shape | 12.763311 | -0.272139 | 12.763311 | 0.851472 | 5.217960 | -0.641417 | 9.285670 | -1.824207 | 2.754427 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 225 | N19 | RF_ok | nn Shape | 13.692040 | 0.057299 | 13.692040 | 0.866734 | 6.592055 | -0.762275 | 8.900217 | -1.320179 | 1.160762 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 225 | N19 | RF_ok | nn Shape | 13.999973 | -0.109490 | 13.999973 | 0.844381 | 5.886205 | -0.572624 | 9.159324 | -1.395213 | 0.970558 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 225 | N19 | RF_ok | nn Shape | 13.667947 | -0.505806 | 13.667947 | 0.714949 | 5.123507 | -0.852212 | 9.265966 | -1.216207 | 0.762569 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 225 | N19 | RF_ok | nn Shape | 12.838396 | -0.386305 | 12.838396 | 0.943763 | 4.462579 | -0.684553 | 10.660074 | -1.064981 | 2.380908 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 225 | N19 | RF_ok | nn Shape | 15.989284 | -0.532760 | 15.989284 | 0.837437 | 4.863295 | -0.718906 | 10.462438 | -1.054365 | 0.911865 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 225 | N19 | RF_ok | nn Shape | 13.181641 | 13.181641 | -0.497594 | 4.614999 | 0.785641 | 10.766344 | -0.680391 | 0.614324 | -1.290638 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 225 | N19 | RF_ok | nn Shape | 13.318005 | 13.318005 | -0.622871 | 4.337721 | 0.901973 | 9.017480 | -0.767606 | 0.329169 | -1.196924 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 225 | N19 | RF_ok | nn Shape | 15.617793 | 15.617793 | -0.672815 | 4.681188 | 0.799478 | 12.895525 | -0.887185 | 0.499788 | -1.172455 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 225 | N19 | RF_ok | nn Shape | 13.053163 | -0.562505 | 13.053163 | 0.738944 | 4.852280 | -0.790846 | 7.754582 | -1.342791 | 1.820509 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 225 | N19 | RF_ok | nn Shape | 15.975549 | 15.975549 | -0.411426 | 5.155710 | 0.824322 | 10.956474 | -0.845666 | 9.022449 | -0.767693 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 225 | N19 | RF_ok | nn Shape | 14.390586 | 14.390586 | -0.461000 | 4.875191 | 0.680884 | 8.345335 | -0.995882 | 1.865166 | -1.637674 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 225 | N19 | RF_ok | nn Shape | 13.784398 | -0.368139 | 13.784398 | 0.827147 | 5.159750 | -0.198687 | 11.781676 | -0.084885 | 3.775051 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 225 | N19 | RF_ok | nn Shape | 13.628506 | -0.669788 | 13.628506 | 0.728838 | 4.631454 | -1.114246 | 10.863366 | -0.999414 | 5.711964 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 225 | N19 | RF_ok | nn Shape | 11.406553 | -1.179010 | 11.406553 | 0.116680 | 4.106878 | -0.982090 | 5.147744 | -1.052124 | 2.741084 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 225 | N19 | RF_ok | nn Shape | 12.610679 | 12.610679 | -0.696491 | 4.739767 | 0.885659 | 12.057906 | -0.864181 | 0.831085 | -1.473871 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 225 | N19 | RF_ok | nn Shape | 12.062659 | 12.062659 | -0.981217 | 4.440364 | 0.506421 | 10.547372 | -1.209325 | 4.589632 | -0.458912 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 225 | N19 | RF_ok | nn Shape | 12.702381 | -0.871378 | 12.702381 | 0.536967 | 4.026978 | -0.703798 | 9.843934 | -1.288309 | 0.587711 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 225 | N19 | RF_ok | nn Shape | 12.887848 | 12.887848 | -0.819833 | 4.331719 | 0.732450 | 10.697958 | -0.655372 | 1.168815 | -1.197168 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 225 | N19 | RF_ok | nn Shape | 13.566399 | -0.469378 | 13.566399 | 0.561880 | 4.505508 | -0.635719 | 10.956538 | -1.339956 | 1.209230 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 225 | N19 | RF_ok | nn Shape | 13.103691 | 13.103691 | -0.650795 | 4.511652 | 0.621683 | 10.589717 | -0.658617 | 1.386269 | -1.013523 |